Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3APPL8V2SZ" target="_blank" >RIV/00216208:11320/23:PPL8V2SZ - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172014448&doi=10.1007%2f978-3-031-40498-6_6&partnerID=40&md5=4028296fb87c614e412d23ab8d6349f6" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172014448&doi=10.1007%2f978-3-031-40498-6_6&partnerID=40&md5=4028296fb87c614e412d23ab8d6349f6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-40498-6_6" target="_blank" >10.1007/978-3-031-40498-6_6</a>
Alternative languages
Result language
angličtina
Original language name
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
Original language description
"This paper presents a set of industrial-grade text processing models for Hungarian that achieve near state-of-the-art performance while balancing resource efficiency and accuracy. Models have been implemented in the spaCy framework, extending the HuSpaCy toolkit with several improvements to its architecture. Compared to existing NLP tools for Hungarian, all of our pipelines feature all basic text processing steps including tokenization, sentence-boundary detection, part-of-speech tagging, morphological feature tagging, lemmatization, dependency parsing and named entity recognition with high accuracy and throughput. We thoroughly evaluated the proposed enhancements, compared the pipelines with state-of-the-art tools and demonstrated the competitive performance of the new models in all text preprocessing steps. All experiments are reproducible and the pipelines are freely available under a permissive license. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG."
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
"Lect. Notes Comput. Sci."
ISBN
978-303140497-9
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
58-69
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
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Event location
Cham
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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